





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


__A							   =							{
    "configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A							   =							[
         "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
         "GPTNeoForCausalLM",
         "GPTNeoForQuestionAnswering",
         "GPTNeoForSequenceClassification",
         "GPTNeoForTokenClassification",
         "GPTNeoModel",
         "GPTNeoPreTrainedModel",
         "load_tf_weights_in_gpt_neo",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A							   =							[
         "FlaxGPTNeoForCausalLM",
         "FlaxGPTNeoModel",
         "FlaxGPTNeoPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gpt_neo import (
              GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTNeoForCausalLM,
              GPTNeoForQuestionAnswering,
              GPTNeoForSequenceClassification,
              GPTNeoForTokenClassification,
              GPTNeoModel,
              GPTNeoPreTrainedModel,
              load_tf_weights_in_gpt_neo,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
     import sys

     __A							   =							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


'''simple docstring'''




import numpy as np






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       str:
    __lowerCamelCase            =    int(np.ceil((x_end - xa) / h							)							)
    __lowerCamelCase            =    np.zeros((n + 1,)							)
    __lowerCamelCase            =    ya
    __lowerCamelCase            =    xa

    for k in range(UpperCamelCase__							):
        __lowerCamelCase            =    f(UpperCamelCase__							,    y[k]							)
        __lowerCamelCase            =    f(x + 0.5 * h							,    y[k] + 0.5 * h * ka							)
        __lowerCamelCase            =    f(x + 0.5 * h							,    y[k] + 0.5 * h * ka							)
        __lowerCamelCase            =    f(x + h							,    y[k] + h * ka							)
        __lowerCamelCase            =    y[k] + (1 / 6) * h * (ka + 2 * ka + 2 * ka + ka)
        x += h

    return y


if __name__ == "__main__":
  import doctest

  doctest.testmod()
